code: def train(
      cls,
      model_filename,
      featuresets,
      classifier="naivebayes",
      options=[],
      quiet=True,
  ):
      # Make sure we can find java & weka.
      config_weka()
  
      # Build an ARFF formatter.
      formatter = ARFF_Formatter.from_train(featuresets)
  
      temp_dir = tempfile.mkdtemp()
      try:
          # Write the training data file.
          train_filename = os.path.join(temp_dir, "train.arff")
          formatter.write(train_filename, featuresets)
  
          if classifier in cls._CLASSIFIER_CLASS:
              javaclass = cls._CLASSIFIER_CLASS[classifier]
          elif classifier in cls._CLASSIFIER_CLASS.values():
              javaclass = classifier
          else:
              raise ValueError("Unknown classifier %s" % classifier)
  
          # Train the weka model.
          cmd = [javaclass, "-d", model_filename, "-t", train_filename]
          cmd += list(options)
          if quiet:
              stdout = subprocess.PIPE
          else:
              stdout = None
          java(cmd, classpath=_weka_classpath, stdout=stdout)
  
          # Return the new classifier.
          return WekaClassifier(formatter, model_filename)
  
      finally:
          for f in os.listdir(temp_dir):
              os.remove(os.path.join(temp_dir, f))
          os.rmdir(temp_dir)
  
imports: import os
  import re
  import subprocess
  import tempfile
  import time
  import zipfile
  from sys import stdin
  from nltk.classify.api import ClassifierI
  from nltk.internals import config_java, java
  from nltk.probability import DictionaryProbDist
label_desc: Use image node_id 7 for calling the WekaClassifier obj's underlying member method code with example usage: obj.train(cls, model_filename, featuresets, classifier, options, quiet) and returns: WekaClassifier